Machine Learning for Ecology and Sustainable Natural Resource Management

✍ Scribed by Grant Humphries, Dawn R. Magness, Falk Huettmann

• Publisher: Springer
• Year: 2018
• Tongue: English
• Leaves: 442
• Edition: 1
• Category: Library

✦ Synopsis

Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often “messy” and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.

✦ Table of Contents

Front Matter ....Pages i-xxiv
Front Matter ....Pages 1-1
Machine Learning in Wildlife Biology: Algorithms, Data Issues and Availability, Workflows, Citizen Science, Code Sharing, Metadata and a Brief Historical Perspective (Grant R. W. Humphries, Falk Huettmann)....Pages 3-26
Use of Machine Learning (ML) for Predicting and Analyzing Ecological and ‘Presence Only’ Data: An Overview of Applications and a Good Outlook (Falk Huettmann, Erica H. Craig, Keiko A. Herrick, Andrew P. Baltensperger, Grant R. W. Humphries, David J. Lieske et al.)....Pages 27-61
Boosting, Bagging and Ensembles in the Real World: An Overview, some Explanations and a Practical Synthesis for Holistic Global Wildlife Conservation Applications Based on Machine Learning with Decision Trees (Falk Huettmann)....Pages 63-83
Front Matter ....Pages 85-85
From Data Mining with Machine Learning to Inference in Diverse and Highly Complex Data: Some Shared Experiences, Intellectual Reasoning and Analysis Steps for the Real World of Science Applications (Falk Huettmann)....Pages 87-108
Ensembles of Ensembles: Combining the Predictions from Multiple Machine Learning Methods (David J. Lieske, Moritz S. Schmid, Matthew Mahoney)....Pages 109-121
Machine Learning for Macroscale Ecological Niche Modeling - a Multi-Model, Multi-Response Ensemble Technique for Tree Species Management Under Climate Change (Anantha M. Prasad)....Pages 123-139
Mapping Aboveground Biomass of Trees Using Forest Inventory Data and Public Environmental Variables within the Alaskan Boreal Forest (Brian D. Young, John Yarie, David Verbyla, Falk Huettmann, F. Stuart Chapin III)....Pages 141-160
Front Matter ....Pages 161-161
‘Batteries’ in Machine Learning: A First Experimental Assessment of Inference for Siberian Crane Breeding Grounds in the Russian High Arctic Based on ‘Shaving’ 74 Predictors (Falk Huettmann, Chunrong Mi, Yumin Guo)....Pages 163-184
Landscape Applications of Machine Learning: Comparing Random Forests and Logistic Regression in Multi-Scale Optimized Predictive Modeling of American Marten Occurrence in Northern Idaho, USA (Samuel A. Cushman, Tzeidle N. Wasserman)....Pages 185-203
Using Interactions among Species, Landscapes, and Climate to Inform Ecological Niche Models: A Case Study of American Marten (Martes americana) Distribution in Alaska (Andrew P. Baltensperger)....Pages 205-225
Advanced Data Mining (Cloning) of Predicted Climate-Scapes and Their Variances Assessed with Machine Learning: An Example from Southern Alaska Shows Topographical Biases and Strong Differences (Falk Huettmann)....Pages 227-241
Using TreeNet, a Machine Learning Approach to Better Understand Factors that Influence Elevated Blood Lead Levels in Wintering Golden Eagles in the Western United States (Erica H. Craig, Tim H. Craig, Mark R. Fuller)....Pages 243-260
Front Matter ....Pages 261-261
Breaking Away from ‘Traditional’ Uses of Machine Learning: A Case Study Linking Sooty Shearwaters (Ardenna griseus) and Upcoming Changes in the Southern Oscillation Index (Grant R. W. Humphries)....Pages 263-283
Image Recognition in Wildlife Applications (Dawn R. Magness)....Pages 285-294
Machine Learning Techniques for Quantifying Geographic Variation in Leach’s Storm-Petrel (Hydrobates leucorhous) Vocalizations (Grant R. W. Humphries, Rachel T. Buxton, Ian L. Jones)....Pages 295-312
Front Matter ....Pages 313-313
Machine Learning for ‘Strategic Conservation and Planning’: Patterns, Applications, Thoughts and Urgently Needed Global Progress for Sustainability (Falk Huettmann)....Pages 315-333
How the Internet Can Know What You Want Before You Do: Web-Based Machine Learning Applications for Wildlife Management (Grant R. W. Humphries)....Pages 335-351
Machine Learning and ‘The Cloud’ for Natural Resource Applications: Autonomous Online Robots Driving Sustainable Conservation Management Worldwide? (Grant R. W. Humphries, Falk Huettmann)....Pages 353-377
Assessment of Potential Risks from Renewable Energy Development and Other Anthropogenic Factors to Wintering Golden Eagles in the Western United States (Erica H. Craig, Mark R. Fuller, Tim H. Craig, Falk Huettmann)....Pages 379-407
Front Matter ....Pages 409-409
A Perspective on the Future of Machine Learning: Moving Away from ‘Business as Usual’ and Towards a Holistic Approach of Global Conservation (Grant R. W. Humphries, Falk Huettmann)....Pages 411-430
Back Matter ....Pages 431-441